Method for computing a target setting value

ABSTRACT

In a method for computing a target setting value—which is adapted to a harvesting process—for a control parameter of a working unit of a harvesting machine, operating-result curves are plotted for a plurality of different operating-result parameters as a function of the related control parameter, a target setting value of the control parameter is subsequently computed based on a combination of the plotted operating-result curves, and a method and a corresponding control unit for controlling a working unit of a harvesting machine, and a harvesting machine with a control unit of this type are also provided.

BACKGROUND OF THE INVENTION

The present invention relates to a method for computing a target settingvalue—which is adapted to a harvesting process—for a control parameterof a working unit of a harvesting machine, in particular a combineharvester. The present invention further relates to a method and acorresponding control unit for controlling a working unit of aharvesting machine, and a harvesting machine with a control unit of thistype.

Modern agricultural harvesting machines, in particular self-propelledharvesting machines such as combine harvesters, forage harvesters, etc.,include one or more adjustable working units for processing varioustypes of crops. With modern harvesting machines, the individual unitsare equipped with adjusting devices—which are usually remotelycontrollable from the driver's cab—with which various control parametersof the working units can be set. Typical working units of a combineharvester are, e.g., the threshing mechanism, which usually includes aconcave and one or more cylinders, and a cleaning unit locateddownstream of the threshing mechanism, the cleaning unit typicallyincluding a blower and a plurality of sieves. Different types of cropsand harvesting conditions, such as moisture, crop height, groundconditions, etc., require that the individual units and/or theiradjustable control parameters be adjusted as exactly as possible to thespecific, on-going harvesting process, in order to obtain an optimumoperating result overall.

Despite the many setting aids offered to operators by the manufacturersof harvesting machines—such as comprehensive operator training, printedlists of setting values predetermined for various harvesting situationsthat the operator can refer to, and electronic tools such as electronicfieldwork information systems preprogrammed with optimized combinationsof setting values for highly diverse harvesting situations for theoperator to choose from—it is still relatively difficult for operatorsto adjust the machine such that it functions in an optimum manner inaccordance with the desired requirements. This is the case, inparticular, for inexperienced and/or untrained operators, particularlyat the beginning of a harvesting season. In many cases, therefore, theharvesting machine and/or its working units are not adapted to thecurrent harvesting process in an optimum manner. As a result, theavailable harvesting capacity of the machine is under-utilized, pooroperating results are obtained, or, in some cases, unnecessary croplosses result.

To solve this problem, DE 101 47 733 A1 provides an automated method forcomputing a setting for an agricultural harvesting machine which hasbeen adapted to the harvesting process. With this method, one controlparameter of the harvesting machine is varied while the setting remainsthe same and the harvesting conditions are the same. The operatingresults are subsequently compared to select exactly that setting valuefor the particular control parameter that delivered a better operatingresult. Using this method, even inexperienced operators learn relativelyquickly whether, when and to what extent the varied control parameteraffects the operating result, and they can set the control parameteraccordingly. The setting can also be carried out automatically, ofcourse. The operating-result values can be recorded, in particular, and,by referring to the recorded operating results, a relationship betweenthe varied setting parameter and the operating result obtained can beidentified. Based on this relationship, an optimum setting parameterthat leads to the best operating result can then be selected.

Since a system is involved with most of the working units on harvestingmachines, however, setting one control parameter affects highly diverseoperating-result parameters. For example, setting a blower speed—whichis a single control parameter of the cleaning unit of a combineharvester—influences not only the losses due to cleaning, but also thetotal tailings and grain tailings. The tailings are the crop materialcomponents that are returned to the threshing unit to be threshed again.A distinction is made between total tailings, which is the totalquantity of tailings, and grain tailings, which refers to the grainportion of the total tailings. The losses due to cleaning are theportions of grain carried out of the machine with the non-graincomponents as a loss. A main objective of selecting the setting, ofcourse, is to keep losses to a minimum. Since tailings place anadditional load on the threshing unit, however, the quantity of tailingsshould also be a minimum, in the ideal case. Unfortunately, it is notnecessarily the case that, when the blower speed is varied from acertain point outward in a certain direction, that all of the variousoperating-result parameters mentioned above, e.g., losses due tocleaning, total tailings and grain tailings, are automatically improved,since the minimum values of the various operating-result parameters arenot all located at the same blower speed. This example also applies forother control parameters of the cleaning unit, e.g., the upper sievewidth setting and the lower sieve width setting, and for a large numberof other working units and their control parameters. In most cases, thevarious operating-result parameters are a not a function of just onecontrol parameter, but of a large number of control parameters.Conversely, changing one control parameter affects a plurality ofoperating-result parameters.

SUMMARY OF THE INVENTION

It is therefore an object of the present invention to create an improvedtarget setting value determination method, and a method and control unitfor controlling a working unit of a harvesting machine that permit themost reliable, simple and automatable selection of a target settingvalue, even when very complex setting dependencies are involved, thetarget setting value being optimally adapted to the particularharvesting process, and to therefore permit optimized control of theworking unit.

In keeping with these objects and with others which will become apparenthereinafter, one feature of the present invention resides, brieflystated, in a method for computing a target setting value (ZG, ZO, ZU)which has been adjusted according to a harvesting process, for a controlparameter (SG, SO, SU) of a working unit of a harvesting machine, themethod comprising the steps of plotting operating-result curves (KR, KK,KV) for a plurality of different operating-result parameters as afunction of a related control parameter (SG, SO, SU); and, based on acombination of the operating-result curves (KR, KK, KV), computing thetarget setting value (ZG, ZO, ZU) of the control parameter (SG, SO, SU).

Another feature of the present invention resides, briefly stated, in amethod for controlling a working unit of a harvesting machine,comprising a first step including computing a target setting value (ZG,ZO, ZU) for a control parameter (SG) of the working unit as defined inclaim 1; and subsequently controlling the working unit based on a targetsetting value (ZE) that was determined.

Still a further feature of the present invention resides in a controlunit for controlling a working unit of a harvesting machine, comprisinga number of measured-value inputs for acquiring operating-resultmeasured values (MR, MK, MV) of various operating-result parameters ofthe working unit; a curve calculating unit for computingoperating-result curves (KR, KK, KV) for the various operating-resultparameters, each of which is based on a number of the operating-resultmeasured values (MR, MK, MV) of a particular operating-result parameteracquired at various setting values of a certain control parameter (SG,SO, SU) of the working unit; a target setting value detection unit forcomputing a target setting value (ZG, ZO, ZU) adapted to a harvestingprocess for the control parameter (SG, SO, SU) based on a combination ofthe operating-result curves (KR, KK, KV) of the variouscomputed-operating result parameters; and a control parameter output forcontrolling an operation selected from the group consisting ofcontrolling the working unit based on the computed target setting value(ZG, ZO, ZU); offering the computed target setting value (ZG, ZO, ZU) toan operator to use in controlling the working unit, and both.

According to the present invention, in order to compute an optimumtarget setting value for a certain control parameter, the first step isto plot operating-result curves for each of a plurality of variousoperating-result parameters as a function of the particular controlparameters. The target setting value of the control parameter is thencomputed based on a combination of the operating-result curves that wereplotted.

Combining the operating-result curves ensures that, even when thevarious operating-result parameters have very complex dependencies onthe particular control parameters, an optimum target setting value isstill found for the current harvesting conditions, thereby ensuring thatthe machine achieves an operating result that is optimum overall for thegiven conditions.

In the method according to the present invention for controlling aworking unit of a harvesting machine, this target setting value—whichwas computed as described above—is used as the “setpoint” for theparticular control parameters, in order to control the working unit.

A control unit according to the present invention requires, among otherthings, a number of measured-value inputs in order to acquireoperating-result measured values for various operating-result parametersof the working unit. This control unit also requires a curve calculatingunit to plot operating-result curves for the various operating-resultparameters, each curve being based on a number of operating-resultmeasured values for the particular operating-result parameter acquiredat various setting values of a certain control parameter of the workingunit. A control unit of this type must also include a target settingvalue computation unit in order to compute a target setting value—whichhas been adapted to a harvesting process—for the control parameter basedon a combination of the plotted operating-result curves for the variousoperating-result parameters. Finally, the control unit requires acontrol-parameter output to control the working unit directly based onthe computed target setting value, or to at least offer an operator thesetting values of the particular control parameter so he can make aselection for control purposes. With a control unit of this type, theparticular control parameter can be automatically adapted to the currentharvesting conditions in an optimum manner, and the operator need nothave extensive experience in doing this.

A control unit of this type can be designed in the form of aprogrammable microprocessor, in particular, the curve calculating unitand the target setting value computation unit being implemented in theform of software on this processor. It is also possible to design anexisting programmable control unit of a harvesting machine according tothe present invention by implementing units realized in the form ofsoftware modules, provided this control unit includes an appropriatenumber of measured value inputs for acquiring the requiredoperating-result measured values and the corresponding control-parameteroutputs. The required software components and/or all required programcode means can be loaded directly into the memory of the programmablecontrol unit, e.g., using a data memory, as a computer program product,in the form of an update in particular.

The method for controlling a working unit can also be refined inaccordance with the method for computing a target setting value, andvice versa. The control unit can also be refined in accordance with thedependent method claims.

To plot an operating-result curve, operating-result measured values arepreferably acquired for a number of various setting values of thecontrol parameter. A mathematical function is then adapted to theoperating-result measured values as a function of the setting values,the mathematical function ultimately forming the operating-result curve.

The measured values can be preferably acquired by measuring theparticular control parameter alternately at high and low setting values.In this manner, the situation can be prevented in which, during extendedoperation in a certain working range of the control parameter,systematic measurement errors are prevented from forming and being addedaccumulatively. This also prevents the possibility of the working unitsbecoming overloaded when, e.g., operating-result measured values must beacquired in an extreme working range of the control parameter.

The number and scattering of measured values, i.e., the working rangeacross which the setting values of the control parameter for recordingthe measured values vary, depends on the circumstances of the particularmeasurement, the type of control parameter, the type of operating-resultparameter, and, possibly, on the mathematical function to be adapted,including, in particular, any advance knowledge of the curve to beexpected. A fixed number of setting values can be specified in advance,for example. It can also be specified in advance that exactly certainsetting values of the control parameter must be applied to acquire theoperating-result measured values. It is also possible to select thenumber and position of the setting values as a function of currentconditions and/or on the basis of advance knowledge of previousoptimization cycles, etc., especially for use in the current computationof the target setting value. It must be taken into account that a largevariance, i.e., the broadest possible range of variation of the controlparameter, has the advantage that it increases the level of certaintywith which a mathematical function that describes the actual curve asexactly as possible can be graphed. On the other hand, performing ameasurement within a small range of variation has the advantage thatmeasuring time is shortened and it is not necessary to work in extremeloss ranges while the measurement is being performed, provided, e.g.,that losses must be measured as operating results. As a result of themethod according to the present invention for computing the settingvalue, the losses that occur during the optimization process itself areless substantial.

Highly diverse fit methods can be used to calculate a mathematicalfunction that is adapted to the operating-result measured valuesdepending on the type of scattering of the operating-result measuredvalues and the shape of the curve that is expected. It can be ensuredthat, regardless of the form, deviations of the measured values from thecurve are minimized, but “outliers” among the measured values do notmatter very much.

With a particularly preferred exemplary embodiment, the operating-resultmeasured values are subjected to a regression analysis for this purpose.In order to describe a linear dependency of the operating-resultmeasured value on the control parameter, a linear regression method canbe used, for example, to describe a parabolic dependency, i.e., aquadratic regression. To ensure that a physically meaningful assertioncan be made, at least four measured values should be applied in aquadratic regression. Particularly preferably, operating-result measuredvalues are plotted for five different setting values of the controlparameter, however. This is a very good compromise between minimizingthe measuring time and obtaining the necessary number of test points inorder to generate a parabolic operating-result curve with informativevalue. Preferably, all operating-result measured values for variousoperating-result parameters are determined simultaneously as a functionof the varied control parameter. This means, e.g., the control parameteris set for a certain measurement setting value, then theoperating-result measured values are acquired in parallel for alloperating-result parameters that are dependent on this measurementsetting value. Measurement time is shortened considerably in thismanner. All operating-result parameters can also be measuredindependently, of course, provided this would be a meaningful approachfor certain reasons in a specific case.

Preferably, when a target setting value is computed, a default valuespecified by the operator, for example, can be taken into account. Theoperator of the harvesting machine can therefore determine whether acertain operating-result parameter is more important than otheroperating-result parameters for the on-going harvesting process. Forexample, the option to select either “increased cleanliness” or“increased cleaning output” can also be provided among the settings fora cleaning unit on a combine harvester. If increased cleanliness isselected via the default value, a narrower sieve setting than the targetsetting value can be selected, for instance. If increased cleaningoutput is required, a somewhat wider sieve opening than the targetsetting value is selected.

There are highly diverse methods for computing the target setting valuebased on a combination of the plotted operating-result curves.

With a preferred exemplary embodiment, a curve-specific target settingvalue or a curve-specific target setting value range, e.g., a rangebelow or above a certain threshold value capable of being determinedusing the curve, is initially computed separately for each of theoperating-result curves. These curve-specific target setting values ortarget setting value ranges are then linked with each other in asuitable manner.

With a preferred variation, the extreme values and/or inflection pointsof the operating-result curves are calculated in order to determine thetarget setting value of the control parameter, and they are linkedaccording to a predetermined rule. In other words, the curve-specifictarget setting values or target setting value ranges are defined in thiscase by the extreme values and/or inflection points.

With cleaning-loss curves, tailings curves, or curves for otheroperating-result parameters, the purpose of which is to keep themeasured values as low as possible, the minimum values of theoperating-result curve are calculated, for example, and linked accordingto a predetermined rule. With operating-result parameters defined toattain the highest results possible, e.g., in terms of the quantity ofcrop material that is conveyed, the maximum values of the particularoperating-result curves can be used.

A link of this type can be carried out, e.g., by calculating the mean ofthe extreme values or inflection points of the operating-result curves.A weighted mean can also be calculated.

With a preferred exemplary embodiment, the extreme value and/or theinflection point of at least one of the operating-result curves is actedupon with an offset value, e.g., before the mean is calculated. Thisoffset value can be selected, e.g., by compensating once more forsystematic errors that are unavoidable, e.g., due to the position ofsensors with which the operating-result measured values are recorded, orfor other reasons. An offset value of this type can also be used toweight various curves with respect to each other.

In particular, an offset value of this type can also be selected as afunction of the default value entered in advance by the operator, inorder to obtain a target setting value that corresponds to therequirement, e.g., for increased cleanliness or increased cleaningoutput in the case of a cleaning unit, for example.

The offset value can be selected, preferably, as a function of the slopeof the operating-result curve in the particular range. As such, themanner in which the application of the offset affects the particularoperating result can be taken into account.

As an alternative to the linking of extreme values or inflection pointsdescribed above, it is also possible (as mentioned briefly, above) todetermine a threshold value based on an initial operating-result curveand to use this threshold value to compute the target setting value fora second operating-result curve in a supplementary manner. The targetsetting value then depends primarily on the second operating-resultcurve, but it does not lie above or below the threshold value specifiedvia the first operating-result curve.

If the working unit includes a cleaning unit, or if the working unit isa cleaning unit, separating curves are preferably plotted asoperating-result curves for the cleaning losses and/or grain tailingsand/or the total tailings. In this case, the blower speed and/or theupper-sieve opening width of the cleaning unit are then preferably setbased on all three separating curves. The lower-sieve opening width ofthe cleaning device is preferably set, however, based only theseparating curves for the grain tailings and the total tailings.

Since, with working units such as a threshing mechanism and/or acleaning unit in particular, the operating-result measured valuesdepend, to a great extent, on the throughput, which, in turn dependsprimarily on the crop quantity and ground speed, these harvestingconditions are held constant within a certain tolerance range over apredetermined measurement period in order to acquire the mostunequivocal operating-result measured value possible. It is thereforepreferably ensured that, once the measurement has started, the driverholds the speed as constant as possible while the measurement is beingcarried out. An automated method of holding the harvesting conditionsconstant can also be provided, of course.

Advantageously, measurement of an operating-result measured value isautomatically interrupted when the harvesting machine is driven out of afield to be harvested, and is automatically restarted when theharvesting machine is driven back onto the field to be harvested.Various possibilities for keeping the harvesting conditions as constantas possible over the predetermined measurement period, for automaticallyinterrupting it when the harvesting machine is driven out of a field tobe harvested and restarting it when the harvesting machine is drivenback onto the field to be harvested will be described in greater detailbelow.

The target setting values computed in a manner described according tothe present invention are used, as mentioned above, in a methodaccording to the present invention for the automated control of aworking unit of the harvesting machine by controlling the working unitbased on the computed target setting value.

According to this method, target setting values for various controlparameters of the working unit are preferably computed in succession. Aninitial target setting value for a first control parameter is computed,then the working unit is initially controlled based on the computedtarget setting value. A further target setting value is then computedfor a further control parameter of the working unit, and it is also set.This method is continued until all control parameters have been set inan optimum manner. Instead of the working unit being controlledimmediately and automatically with the computed target setting value,the computed target setting value can first be offered to the operatorfor selection, e.g., in a display. The operator can accept the value,e.g., by entering a confirmation command.

If a cleaning unit is controlled using the method according to thepresent invention, a target setting value for a blower speed ispreferably computed in a first step, a target setting value for theupper-sieve opening width is computed in a second step, and a targetsetting value for a lower-sieve opening width of the cleaning device iscomputed in a third step. The particular components are then preferablycontrolled immediately using the computed target setting value.

The initial setting values to be used in an optimization of this typeare preferably grain-dependent setting values predetermined by themanufacturer of the harvesting machine for certain crops under certainharvesting conditions, or that were computed in advance by the operatorof the harvesting machine under similar conditions for the crop beingharvested. In particular, crop-dependent setting values stored in anelectronic fieldwork computer system can be used as the initial settingvalues. In the optimization process, these initial setting values arefirst entered for the various control parameters One of the firstcontrol parameters is then varied, the associated measured values areplotted, and an optimum setting value for this control parameter is thenidentified in a manner according to the present invention. A secondcontrol parameter is then optimized in this manner, etc., until all ofthe desired control parameters have been optimized. The operator candetermine which of the control parameters to optimize. Normally, allcontrol parameters are optimized, to the extent this is possible.

Preferably, after a certain period of time and/or when a predeterminedevent occurs, a new target setting value for a control parameter of theworking unit is computed, and the working unit is controlled based onthe new setting value. It is also possible, of course, to recompute anentire chain of target setting values for various control parameters ofthe working unit, for example, as described above.

The certain time period can be selected such that optimization iscarried out Whenever it is expected that the harvesting conditions havechanged. For example, with a harvesting process that takes an entire dayto complete, reoptimization can be carried out in the morning, in theafternoon and in the evening, because it is possible that the strawmoisture in the crop material changed over the course of the day.

The events that could make it necessary to reoptimize the target settingvalues can include, in particular, a change in throughput, e.g., ifharvesting is carried out at a speed that differs from the speed thatexisted when the target setting values were determined. The event canalso be a change to a control parameter of another working unit of theharvesting machine. It can be assumed, for example, that the cleaningload in a combine harvester changes considerably if the threshingmechanism was repositioned to a considerable extent. Events can also bepredetermined via other measurement sensors, so that the target settingvalues are recomputed, e.g., depending on the crop, i.e., when a changedproperty of the crop, such as grain moisture, is measured.

When a recomputation of the target setting values is started, i.e., forthe re-start of optimization, the target setting values computed in theprevious optimization are preferably used as the default setting for themeasurement.

The novel features which are considered as characteristic for thepresent invention are set forth in particular in the appended claims.The invention itself, however, both as to its construction and itsmethod of operation, together with additional objects and advantagesthereof, will be best understood from the following description ofspecific embodiments when read in connection with the accompanyingdrawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a schematic cross section through a combine harvester,

FIG. 2 shows a schematic depiction of a control unit for controlling acleaning unit of a combine harvester that includes a connected controlterminal with a user interface, in a first process state,

FIG. 3 shows a depiction of a user interface of the control terminalaccording to FIG. 2, in a second process state,

FIG. 4 shows a depiction of a user interface of the control terminalaccording to FIG. 2, in a third process state,

FIG. 5 shows a flow chart of a possible sequence of steps in anoptimization of the cleaning unit of a combine harvester,

FIG. 6 shows a diagram of a possible sequence of an optimization of oneof the control parameters within a method sequence according to FIG. 5,

FIG. 7 shows a flow chart for computing an optimized target settingvalue for a control parameter of a cleaning unit,

FIG. 8 shows a diagram that depicts the plotting of operating-resultcurves based on operating-result measured values,

FIG. 9 shows a diagram that depicts the identification of an optimizedtarget setting value for the blower speed of a cleaning unit,

FIG. 10 shows a diagram that depicts the identification of an optimumtarget setting value for the upper-sieve opening width,

FIG. 11 shows a diagram that depicts the identification of an optimumtarget setting value for the lower-sieve opening width.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

The exemplary embodiment of the present invention shown in FIG. 1 is aself-propelled combine harvester 1 with a tangential or cross-flowthreshing mechanism 4 and a plurality of shakers 9 located behind it, asthe separating unit. The separating unit is composed of a plurality oftray-type shakers 9 with a plurality of shaking speeds. A cleaning unit10 is located beneath shaker 9, which is composed of a plurality ofsieves 13 located one on top of the other, and a blower 11.

The mode of operation of a combine harvester 1 of this type is asfollows:

Using a reel of the cutting disc, the crop material is placed on mowingunit 2 and is cut using knives. The crop material is then conveyed via aheader auger and a feed rake in a feeder housing 3 to the inlet ofthreshing mechanism 4.

A feed and/or pre-acceleration cylinder 5 is located at the inlet ofthreshing mechanism 4. Located behind threshing mechanism 4, in thedirection of crop flow, is a cylinder 6 with an axis of rotationpositioned transversely to the direction of crop flow, i.e.,transversely to the longitudinal axis of the combine harvester. Locatedbeneath cylinder 6 is a concave 8 which is shaped to encompass cylinder6. The crop material coming out of feeder housing 3 is grasped bypre-acceleration drum 5 and pulled further by cylinder 6 through thethreshing gap between cylinder 6 and concave 8. The crop material isthreshed, i.e., beaten and/or crushed, by the beater bars of cylinder 6,a grain-chaff mixture falling downward through concave 8 and beingsubsequently guided to cleaning unit 10 in order to separate the grainsfrom the admixtures, i.e., stalk and chaff parts.

From threshing mechanism 4, the threshed crop flow is directed byimpeller 7 to tray-type shaker 9, via which the grain and any shortstraw and chaff located in the crop flow is separated out. The grain,short straw and chaff also reach cleaning unit 10, where the grain isseparated from the short straw and chaff.

The grain is separated from the non-grain components in cleaning unit 10in a manner such that wind is blown through the sieve openings (holes,mesh, slits) into sieves 12, 13—which are driven in an oscillatingmanner—using blower 11, the wind loosening the crop material directedover sieves 12, 13 and ensuring that the specifically lighter chaff andshort-straw portions are separated out, while the heavy crop grains fallthrough the sieve openings. An upper sieve 12 and a lower sieve 13 arelocated one on top of the other in certain areas such that the cropmaterial is sifted with different levels of fineness at the variouslevels.

The grain that passes through both sieves 12, 13 of cleaning unit 10falls to a first capture and guide floor and is conveyed to agrain-delivery auger. The grain is then conveyed by an elevator 15 intoa grain tank 19 of combine harvester 1, from where it can be transferredto a trailer as necessary using a tank unloading conveyer.

The particles in cleaning unit 10 that initially fall, at the rear end,through the sieve openings of upper sieve 12 are typically heavierparticles, i.e., particles that contain a grain particle that has notbeen fully separated from other components of the grain. These particlesfall, behind lower sieve 13, onto a second capture and guide floorlocated beneath and somewhat behind the first capture and guide floor,and are returned to threshing mechanism 4 as tailings via a tailingselevator 14.

Components that do not fall through upper sieve 12 are discarded as aloss. The straw and a certain percentage of waste grain also travel viatray-type shaker 9 to the rear end of combine harvester 1, from wherethey are ejected.

With the exemplary embodiment below it is assumed that, according to thepresent invention, the objective is to compute target setting values ZG,ZO, ZU for various control parameters SG, SO, SU of cleaning unit 10 ofa combine harvester, e.g., for the upper-sieve opening width, thelower-sieve opening width, and the blower speed. The method according tothe present invention has already been proven to be effective forsetting a cleaning device 10 of this type, and it can therefore be usedparticularly advantageously. The method according to the presentinvention and/or the corresponding control unit can also be used, ofcourse, to set other working units, e.g., the rotational speed of thecylinder or the width of the concave on any other harvesting machine.For reasons of completeness, reference is also made to the fact that thepresent invention can also be used very well to control cleaning unitson other types of combine harvesters.

To compute target setting values ZG, ZO, ZU adapted to the harvestingprocess for the various control parameters SG, SO, SU of cleaning unit10, various operating-result measured values MR, MK, MV for differentoperating-result parameters must be measured and, based on theseoperating-result measured values MR, MK, MV, operating-result curves KR,KK, KV must be plotted.

With the exemplary embodiment shown, the losses due to cleaning and thetotal tailings and grain tailings are observed as the operating-resultparameters used to compute the target setting values for the variouscontrol parameters. The grain tailings are the grain componentscontained in the total tailings.

Different measuring units 16, 17, 18 are located in various locations inthe combine harvester for this purpose.

A cleaning-loss measuring unit 16 is located directly beneath the rearend of upper sieve 12 and is used to measure the losses due to cleaning,the cleaning-loss measuring unit 16 typically being designed as a knocksensor. The signal detected by knock sensor 16 is a measure of how manycomponents fall directly behind upper sieve 12. Based on thisinformation, the total loss can be estimated relatively well.

The total tailings are measured with the aid of a totaltailings-measuring unit 18 located in tailings elevator 14. It measuresthe total quantity conveyed, e.g., via the weight conveyed by tailingselevator 14 or optical and/or capacitive measurements etc. The grainportion of the total tailings, i.e., the grain tailings, is measuredusing a grain-tailings measuring unit 17 located on the second captureand guide floor behind lower sieve 13. Grain-tailings measuring unit 17is also preferably a knock sensor, the output signal of which is ameasure of the amount of grain that falls behind lower sieve 13 into thetailings.

All of these measuring units 16, 17, 18 are connected to a control unit30. A control terminal 40 is also connected to control unit 30, controlterminal 40 having a display with a user interface 41 with which adriver can operate and/or program control unit 30. Control terminal 40is located inside driver's cab 20. The connection of individualmeasuring units 16, 17, 18 and control terminal 40 with control unit 30,and control unit 30 itself, are not shown in FIG. 1, to prevent thefigure from becoming overly complex. Instead, a somewhat more detaineddepiction is shown in FIG. 2, to which reference is made for theexplanations to follow.

In this case, control unit 30 includes three measured-value inputs 31,32, 33, to which measuring units 16, 17, 18 are connected. Cleaning-lossmeasured values MR are transmitted by cleaning-loss measuring unit 16 toinput 31 of control unit 30, grain-tailings measured values MK aretransmitted by grain-tailings measuring unit 17 to input 32 of controlunit 30, and volume-tailings measured values MV are transmitted byvolume-tailings measuring unit 18 to control unit 30.

Control unit 30 also has three control parameter outputs 35, 36, 37, viawhich the setting values for the control parameters “blower speed” SG,“upper-sieve width setting” SO and “lower-sieve width setting” SU aretransferred as setpoints to the particular components of cleaning unit10. Using appropriate (not shown) sensors, control unit 30 can check todetermine whether the desired setting values were actually attained.

Control terminal 40 is connected to control unit 30 via a terminalinterface 34. In this case, control terminal 40 is designed as atouchpad which the operator can use to press on certain regions of userinterface 41 to enter certain input commands.

In the upper region of user interface 41, three setting-value displayfields 45 for the blower speed, upper-sieve setting and lower-sievesetting (from top to bottom) are depicted, one below the other. Thevalue of control parameter SG, SO, SU that was originally set for theindividual components, and the new target setting value ZG, ZO, ZU aredisplayed in setting-value display fields 45 during operation.

Two default fields 43, 44 are located in the next row down in thedisplay. By pressing default fields 43, 44, the operator can select adefault value VW, which is transmitted to control unit 30. Default valueVW is displayed in a default value display 42 directly below defaultfields 43. When the operator presses left default field 44, defaultvalue VW is reduced. As a result, the optimization process ensures that,in the harvesting process, greater emphasis is placed on “increasedcleanliness” than on cleaning output. Conversely, when the operatorpresses on right default field 43, default value VW is increased, sothat the optimization process places greater emphasis on the criterium“increased cleaning output” than on cleanliness. A start field 46 islocated under default-value display 42. Start field 46 is pressed tostart the optimization process.

It should be noted that user interface 41 can also have a completelydifferent design, of course. In particular, it can also be part of alarger control terminal 40 where even more regions for setting othercomponents are displayed, and where additional information for theoperator is displayed. It is also possible to use another form of userinterface other than a touchpad.

In this case, control unit 30 is designed in the form of a programmablemicroprocessor, on which components which are essential to the presentinvention are implemented in the form of software modules, thecomponents including, e.g., a curve calculating unit 38 which uses inputsignals MR, MK, MV to calculate operating-result curves KR, KK, KV, anda target setting value computation unit 39 which uses curves KR, KK, KVand default value VW set via control terminal 40 to respectivelycalculate the target setting values for the various control parametersSG, so, SU.

A control unit 30 that serves only to control cleaning unit 10 is shownin FIG. 2. It is clear that a control unit 30 of this type can alsocontrol other working units, e.g., the threshing mechanism of combineharvester 1, and that the control units for highly diverse types ofworking units can be located in the form of modules in a master controlunit of combine harvester 1. It is also clear that a control unit ofthis type can also include further measured-value inputs andcontrol-parameter outputs. For example, combine harvester 1 can alsoinclude sensors in the feeder housing for measuring the height of thecrop layer, and/or further sensor units in the grain tank and/or at theoutlet of the grain elevator, such as a yield measuring device fordetermining the total quantity of grain, or grain breakage detectors,with which damaged and/or broken grains can be detected, or sensorslocated at the end of the tray-type shakers for determining the strawwalker losses.

None of these components are depicted in the exemplary embodiment shownin FIG. 2, however, to prevent the figure from becoming overly complex.

Reference is made to FIG. 5 in the explanation of the sequence of stepsthat take place in a complete optimization process of cleaning unit 10.

The process starts when the operator enters a default value VW, asdescribed above. The operator then starts the optimization process. Tostart the optimization process, the operator touches start field 46 oncontrol terminal 40 (refer to FIG. 2). In the next step, a check iscarried out to determine whether the necessary starting conditions aregiven, i.e., whether the threshing units and cleaning unit 10 have beenset. If they have not, the process is halted immediately.

If they have, optimization of the blower speed is started. Settingvalues specified by the electronic fieldwork system for the particulartype of crop are first selected as the starting values for theindividual control parameters.

To measure operating-result curves KR, KK, KV, operating-result measuredvalues KR, KK, KV are then acquired for various setting values M₁, M₂,M₃, M₄, M₅ (also referred to below as “test points”) of the controlparameter to be optimized. Since the objective in this case is to firstoptimize the blower, operating-result measured values MR, MK, MV aremeasured at various measuring points M₁, M₂, M₃, M₄, M₅ of the blowerspeed. While performing a measurement of this type, the other parametersmust not be changed, and any other harvesting conditions must be held asconstant as possible. The applies to throughput in particular.

All of the steps involved in the measurement procedure up to the pointat which optimum target setting value ZG is determined are depictedschematically in FIG. 6. This figure shows a “UML” state diagram whichdescribes the procedure for measuring the blower speed (UML=UnifiedModeling Language; in this UML diagram, the symbols “/” mean “conditionand/or action”, and the symbols “&&” mean “and”.)

First, the system “learns” a constant ground speed. As mentioned above,a uniform flow of crop material during the measurement procedure is animportant requirement for performing an optimization. The throughputquantity depends to a considerable extent on the speed, however. It musttherefore be ensured that the average speed be held as constant aspossible during the entire optimization process. This state, duringwhich the current ground speed is being “learned”, is displayed to theoperator, as shown in FIG. 3, in user interface 41 of control terminal40. In this procedure, the working units use the crop-dependent defaultvalues in the electronic fieldwork system as the starting values. Theseare the basic settings for the units. The starting value of theparticular control parameter is displayed in setting-value displayfields 45. In this case, the starting values are a blower speed of 1,200rpm, an upper-sieve setting of 15 mm, and a lower-sieve setting of 9 mm,which could be used as the starting values when harvesting wheat, forexample.

As soon as a mean ground speed is reached, this is also displayed onuser interface 41 (refer to FIG. 4). On user interface 41, the currentspeed within a tolerance range is also displayed, in a speed-displayfield. The driver must then ensure that the current speed within thistolerance range remains in the middle—as depicted in the display—to thegreatest extent possible.

The Stop field is now displayed instead of the Start field. The operatorcan use the Stop field to stop the optimization process at any time.When he does so, the machine returns to the starting values. Likewise,the operator can use the symbols displayed next to setting-value displayfields 45 to select which of the control parameters, “blower speed” SG,“upper-sieve width” SO, or “lower-sieve width” SU to optimize. Accordingto the standard procedure, as shown in FIG. 5, the blower is optimizedfirst, followed by the upper sieve and then the lower sieve.

Once the desired ground speed has been reached, the first measurementsetting value is applied for the parameter to be optimized, i.e., theblower speed in this case. The system itself is then initially in awaiting state until a start-up phase has been completed, in which theparameters have stabilized once the ground speed and starting valueshave been set. A fixed delay time of, e.g., a few seconds, can bespecified for this. Once the start-up phase has been completed and thecurrent target harvesting conditions have been achieved, measurement ofthe first measured value can be started.

Various measuring units 16, 17, 18 then acquire various operating-resultmeasured values MR, MK, MV at the predetermined, first test point M₁.This means, e.g., a measured value MR for the losses due to cleaning, ameasured value MK for the grain tailings, and a measured value MV forthe total tailings are acquired when a relatively low speed has beenset. This is shown in FIG. 8. In FIG. 8, various measured values MV, MR,MK are shown plotted to the far left for first test point M₁ withrespect to the blower speed. When measured-value acquisition has beencompleted, the next test point, M₅, is applied. After the start-up phasehas been completed, the further measured values for losses due tocleaning, grain tailings and total tailings are acquired. These measuredvalues are also plotted in FIG. 8.

In this case, it is preferably not the next higher test point, M₂, thatis applied, but rather a test point M₅ located at the other end of therange to be measured. This means, e.g., the measurement is first carriedout at the lowest blower speed to be measured, and then at the highestblower speed to be measured. Measurements are then carried out at thesecond-lowest blower speed, followed by the second-highest blower speed,etc. The advantage of performing measurements in an alternating manner,at high and low extreme values, is that, since the losses and tailingsare typically greater in these ranges, the units will not be overloaded,and systematic measurement errors that could result from accumulativelyadded disturbances are prevented.

If the target harvesting conditions stop being met during measured-valueacquisition, e.g., because the machine has driven out of the field to beharvested, the measurement is interrupted and, e.g., the starting valuesspecified by the fieldwork information system can be applied, and themeasured value which has already been measured can be stored. Themachine then remains in the waiting state until the target harvestingconditions are attained again. Measurement setting value M₁, M₂, M₃, M₄M₅ at which the current measurement is to be carried out is then setagain and, after the start-up phase has been completed, measured-valueacquisition is continued.

This interruption of the measurement procedure can take placeautomatically, e.g., with the aid of sensors used to detect the field tobe harvested. Two sensors are preferably used for this purpose. Acrop-layer height can be determined in the feeder using a first sensor.When the machine is driven off of the field, the height of the straw inthe feed rake decreases, practically without any time delay. This sensorcan be used to determine when the machine leaves the field to beharvested, and the measurement can therefore be interrupted immediately.A separate measuring unit, e.g., a grain-throughput measuring unit, ispreferably used to restart the measuring procedure. It checks todetermine whether the grain throughput has climbed above a minimumthreshold again. Since this sensor, which registers the quantity beingconveyed in the upper region of the grain elevator, for example, has adelayed reaction relative to the cleaning unit, the layer of cropmaterial on the upper sieve of the cleaning unit has formed completely,even when this sensor reading is low, thereby indicating with certaintythat the target harvesting conditions are in place again.

Once all measurements have been completed, the optimum target settingvalue ZG is determined in a subsequent step, in the manner according tothe present invention. Reference is made to FIGS. 7 and 8 in theexplanation of this procedure.

In a first step I, mathematical functions are adapted foroperating-result measured values MR, MK, MV, in order to obtainoperating-result curves KR, KK, KV. Since it can be expected, due to thephysical conditions, that curves KR, KK, KV are parabolic in shape, thebest-fit mathematical functions are determined using quadraticregression based on the recursive least squares method. The basic formof a quadratic function of this type is:

y=a ₂ x ² +a ₁ x+b  (1)

The three coefficients a₁, a₂, b for this equation are determined in aregression analysis based on measured values MR, MK, MV that wereobtained. The following equations are used for this purpose, in order tocalculate a factor k and auxiliary variables A through F:

$\begin{matrix}{k = {{{n\left( {\sum\; x_{i}^{2}} \right)}\left( {\sum\; x_{i}^{4}} \right)} + {2\left( {\sum\; x_{i}} \right)\left( {\sum\; x_{i}^{2}} \right)\left( {\sum\; x_{i}^{3}} \right)} - \left( {\sum\; x_{i}^{2}} \right)^{2} - {n\left( {\sum\; x_{i}^{3}} \right)}^{2} - {\left( {\sum\; x_{i}} \right)^{2}\left( {\sum\; x_{i}^{4}} \right)}}} & (2) \\{A = {\left\lbrack {{\left( {\sum\; x_{i}^{2}} \right)\left( {\sum\; x_{i}^{4}} \right)} - \left( {\sum\; x_{i}^{2}} \right)^{2}} \right\rbrack \frac{1}{k}}} & (3) \\{B = {\left\lbrack {{n\left( {\sum\; x_{i}^{4}} \right)} - \left( {\sum\; x_{i}^{2}} \right)^{2}} \right\rbrack \frac{1}{k}}} & (4) \\{C = {\left\lbrack {{n\left( {\sum\; x_{i}^{2}} \right)} - \left( {\sum\; x_{i}} \right)^{2}} \right\rbrack \frac{1}{k}}} & (5) \\{D = {\left\lbrack {{\left( {\sum\; x_{i}^{3}} \right)\left( {\sum\; x_{i}^{2}} \right)} - {\left( {\sum\; x_{i}} \right)\left( {\sum\; x_{i}^{4}} \right)}} \right\rbrack \frac{1}{k}}} & (6) \\{E = {\left\lbrack {{\left( {\sum\; x_{i}} \right)\left( {\sum\; x_{i}^{3}} \right)} - \left( {\sum\; x_{i}^{2}} \right)^{2}} \right\rbrack \frac{1}{k}}} & (7) \\{F = {\left\lbrack {{\left( {\sum\; x_{i}} \right)\left( {\sum\; x_{i}^{2}} \right)} - {n\left( {\sum\; x_{i}^{3}} \right)}} \right\rbrack \frac{1}{k}}} & (8)\end{matrix}$

Using factor k and auxiliary variables A through F, the individualcoefficients of the regression polynomial can be determined, as follows:

b=AΣy ₁ +DΣx _(i) y _(i) +EΣx _(i) ² y _(i)  (9)

a ₁ =DΣy _(i) +BΣx _(i) y _(i) +FΣx _(i) ² y _(i)  (10)

a ₂ =EΣy _(i) +FΣx _(i) y _(i) +CΣx _(i) ² y _(i)  (11)

In the equations shown above, n is the number of test points, x_(i)represents the values of the individual test points, and y_(i)represents the operating result-measured values measured at test pointsx_(i) for the particular operating-results parameter, and i is an indexvariable that counts from 1 to n. Addition is performed accumulativelyfrom i=1 through n.

Curve KR for the losses due to cleaning, curve KK for the graintailings, and curve KV for the total tailings are shown in FIG. 8. Allof the curves decrease initially as the blower speed increases, and theysubsequently start to rise as the blower speed increases. The reason forthis is that, when blower speeds are too low, an excessively thick layerforms on sieves 12, 13, and cleaning unit 10 can no longer operateeffectively. If the blower speed is increased too much, the wind causesan excessive quantity of particles to be carried out of the machine,thereby causing the losses to increase significantly. Likewise, anincreasing quantity of grains that should drop through lower sieve 13are carried into the total tailings, thereby causing grain-tailingscurve KK and volume-tailings curve KV to increase.

Each of the minimum values RM, KM, VM of curves KR, KK, KV is the idealsetting value for the blower with respect to the particular curve KR,KK, KV. Unfortunately, however, minimum values RM, KM, VM are notaligned directly one above the other, which means an optimum targetsetting value ZG must be determined that takes all result parametersinto account in a suitable manner. To this end, minimum values RM, KM,VM of individual curves KR, KK, KV are linked in a suitable manner. Thisstep is preceded, however, by a few inquiries to determine the extent towhich the individual plotted curves KR, KK, KV have informative value.

This process is shown in FIG. 7. Step I is the quadratic regression,which is carried out for all three operating-results parameters, i.e.,the losses due to cleaning, the grain tailings, and the total tailings,in order to plot the three curves KR, KK, KV.

In parallel with this step, curve-specific target setting values ZR, ZK,ZV are initially determined for all three operating-result curves KR,KK, KV. The following inquiries are carried out for this purpose (theexplanations below relate to the handling of the cleaning-loss curve KR,as an example):

For curve KR, a check is initially carried out in Step IIa to determinewhether the shape factor, which is a measure of the curvature of theparabola, is sufficiently great. This means that the shape factor ofmeasured curve KR is compared with a threshold value and, only if thisis the case, the minimum value RM of plotted curve KR is accepted ascurve-specific target setting value ZR for the blower speed in terms oflosses due to cleaning (Step VIIa). If the shape factor is too low, theminimum value RM is not unequivocal, and the informative value of curveKR is very low.

In a further query step, IIIa, a query is therefore made as to whetherthe signal change is sufficiently great. To this end, a check is carriedout to determine whether the difference between the operating-resultvalue in minimum RM of curve KR and an operating-result measured valueat a maximum test point of the particular control parameter—at a blowerspeed test point in this case—exceeds a certain value. If so, it can beassumed that the curve is indeed distinct enough. As a result, in StepVIIa, the minimum value on curve KR is accepted as the optimimcurve-specific target setting value ZR for the blower in terms of thelosses due to cleaning.

If not, a check is carried out in Step IVa to determine whether a signalchange can even be detected. To this end, the difference—describedabove—between minimum RM of operating-result curve KR and anoperating-result measured value is applied once more at a higher testpoint of the particular control parameter and compared with a further,lower threshold value. If the difference is not below this thresholdvalue, it is assumed that the curve does not have informative value, andthe crop-dependent starting value specified by the electronic fieldworksystem is applied as the curve-specific target setting value ZR of thecontrol parameter in terms of the losses due to cleaning (Step Va). Ifthe differential value is below the threshold value, however, i.e., if asignal change cannot be detected, this starting value plus an offsetvalue is used, i.e., the blower speed is increased by a certain value,and this value is used as curve-specific target setting value ZR. Sincethe losses do not increase significantly in this case when the blowerspeed is increased, it makes sense in terms of losses to selectcurve-specific target setting value ZR, since the cleaning output isimproved as a result without having to put up with higher losses.

In Steps IIb through VIIb, the same method is carried out in parallelfor the grain tailings, and in Steps 1 c through VIIc for the totaltailings. The only difference between the two is that, in Step VIIb andVIIc, identified minimum values KM, VM of particular curves KK, KV arealso acted upon with an offset value OK, OV in order to determine theparticular curve-specific target setting values ZK, ZV. Offset valuesOK, OV are used to assign top priority to the losses due to cleaning inthe computation of optimum target setting value ZG, i.e., they are usedto place greater weight on cleaning-loss curve KR, since grain tailingsand total tailings should play a somewhat lesser role in daily operationcompared with the losses due to cleaning.

Offset OK, OV is applied such that the blower speed is increased withrespect to minimum value KM) of grain tailings curve KM, and the blowerspeed is reduced with respect to minimum value KV of total tailingscurve KV. A fixed offset value with respect to the operating-resultparameter is specified in the system. A displacement of this type alongthe measured curve around a fixed operating-result value, i.e., in they-direction of particular curve KK, KV, makes it possible to dynamicallyadapt offset OK, OV with respect to the control parameter, i.e., in thex-direction. This means the actual offset value is a function of theslope of curve KK, KV within the minimum range. If curve KK, KV hassharp curvature, displacement is slight. If curves are relatively flatand the differences in tailings are therefore slight, the displacementis greater.

A displacement of the optimum target setting value of this type withrespect to a certain control parameter can be calculated based on thesolution of the quadratic equation (1), as follows:

In this case, Y_(Min) is the operating-result value in minimum value KM,VM of particular operating-result curve KK, KV, and X_(Neu) is thecurve-specific target setting value ZK, ZV for the control parameterwith respect to this curve KK, KV, which is used instead of identifiedminimum value KM, VM of curve KK, KV. Depending on the sign of thesquare root in the quadratic formula in equation (12), the shift is tothe left (for total tailings) or to the right (for grain tailings).

Default value VW—which the operator entered at the beginning of theoptimization process—can also be applied to the offsets to attain“increased cleaning output” or “increased cleanliness”. Accordingly, theoffsets of grain tailings curve KK and total tailings curve KV areshifted to the left or right. As an alternative, of course, an offset inone direction or the other can also be applied to target setting valueZG computed overall, depending on the default value.

Curve-specific target setting values ZR, ZK, ZM computed in Steps Vathrough VIIa, Vb through VIIb and Vc through VIIc are then averaged inStep VIII. This mean is optimized target setting value ZG for theparticular control parameter, which is blower speed ZG in this case.

This procedure is depicted graphically in FIG. 9. FIG. 9 showsoperating-result curves KR, KK, KV for the losses due to cleaning, thetotal tailings, and grain tailings. The minimum values RM, KM, VM arelabeled on each of three curves KR, KK, KV. In addition, offsets OK, OVand resultant curve-specific target setting value ZK, ZV are plotted forgrain tailings curve KK and total tailings curve KV. Optimum targetsetting value ZG is also labeled; it is computed from the mean ofcurve-specific target setting values ZR, ZK, ZV, which corresponds tothe minimum value on cleaning-loss curve KK. It is also labeled with anarrow in the figure.

After optimized target setting value ZG has been determined for blowerspeed SG, the blower can be set with target setting value ZG (refer toFIG. 6).

This procedure is followed by optimization of the upper sieve, as shownin FIG. 5, in which case optimized target setting value ZG of blower 11is used. In this case, the starting values for blower speed SG aretherefore previously-determined optimized target setting value ZG and,for all further parameters, the crop-dependent starting values takenfrom the electronic fieldwork information system.

Subsequently, as was the case for blower optimization, measured valuesare determined for the losses due to cleaning, total tailings and graintailings for various settings of upper-sieve width SO. The procedure isexactly the same as the procedure used to compute target setting valueZG for blower speed SG, as was explained with reference to FIGS. 6 and7, i.e., the same method steps are taken, but this time they are used toset the upper sieve width. Curves KR, KK, KV are also plotted for allthree operating-result parameters using quadratic regression (refer toStep I in FIG. 7), and the minimum values are subsequently linked in asuitable manner.

The only difference in terms of determining target setting value ZG forblower speed SG is that an offset OV is set only for total tailings.Minimum value RM, KM of curves KR, KK are used as curve-specific targetsetting value ZR, ZK for the grain tailings and losses due to cleaning.This is depicted graphically in FIG. 10. In this case as well, minimumvalues RM, KM, VM of all three curves KR, KK, KV are indicated andoffset OV and curve-specific target setting value ZV are also indicatedfor total tailings-curve KV. Target setting value ZO for the upper sieveopening which results from the individual values is also shown.

As shown in FIG. 5, after optimization is carried out for the uppersieve, optimization is carried out for the lower sieve. The individualoperating-result values are also measured, in this case, as a functionof the setting for the lower sieve width in a manner similar to thatdepicted in FIG. 6.

Optimum target setting value ZU for lower-sieve opening SU (refer toFIG. 11) is computed only as a function of grain-tailings curve KK andvolume-tailings curve KV, since the lower sieve does not affect thelosses due to cleaning. In addition, for the total tailings, a quadraticcurve is not adapted to the measured values. Instead, linear regressionis used to determine a straight line as characteristic KV, since totaltailings decrease the wider the opening of lower sieve 13 becomes, andit cannot subsequently increase.

Target setting value ZU is therefore determined primarily based onminimum value KM of grain-tailings curve KK, an offset being applied inone direction or another to curve-specific target setting value ZKdetermined there (which corresponds to minimum KM in this case),depending on default value VW. This is done to attain either increasedcleanliness or increased cleaning output. If the objective is toincrease cleaning output, lower sieve 13 is opened somewhat wider. Ifthe objective is to increase cleanliness, the width of the lower sieveopening is reduced.

In this case, the two operating-result curves KK, KV are therefore notlinked by linking minimum values of the two curves KK, KV, but rather byselecting a threshold value SW by referring to a curveKV—volume-tailings curve KV, in this case—that may not be fallen belowin the computation of an optimum target setting value ZU using anothercurve, i.e., grain-tailings curve KK in this case. In this manner it isensured that, even though a value for lower-sieve opening width SU isobtained that is ideal with respect to grain tailings, the totaltailings are not so great that threshing mechanism 4 is overloaded,which would reduce the total output of machine 1.

After optimization of the lower sieve has been carried out, a waitensues to determine whether an event will occur that would require thatoptimization be repeated (refer to FIG. 5). An automatic restart of theoptimization process can take place in a time-dependent manner, forexample, when it can be assumed that the harvesting conditions havechanged. The restart can take place in a throughput-dependent manner if,e.g., the harvesting speed has changed significantly. The restart cantake place in a setting-dependent manner if other settings on thecombine harvester are changed that indicate that the cleaning load haschanged accordingly. The restart can take place in a crop-dependentmanner if, e.g., a change in a crop property such as grain moisture ismeasured. If optimization must indeed be repeated, then thecrop-dependent setting values are not taken from the electronicfieldwork information system to be used as the default values for thestart, but rather target setting values ZG, ZO, ZU that were computed inthe first optimization procedure. Target setting values ZG, ZO, ZU canalso be entered in the electronic fieldwork information system, ofcourse, in which case the harvesting conditions are also preferablyrecorded, to the extent this is possible, so that target setting valuesZG, ZO, ZU computed in an optimization process can also be used as thestarting values in a subsequent harvesting process in which theharvesting conditions are relatively similar to those that prevailedwhen target setting values ZG, ZO, ZU were computed.

For safety reasons, the system is designed such that the driver canmanually override one or all of the machine parameters that were set, atany time during a harvesting operation. Finally, it is pointed out oncemore that the combine harvester shown in the figures, and the controland the specific method described in conjunction therewith are merelyexemplary embodiments that could be modified in a variety of ways by oneskilled in the art, without leaving the framework of the presentinvention.

It will be understood that each of the elements described above, or twoor more together, may also find a useful application in other types ofmethods and constructions differing from the types described above.

While the invention has been illustrated and described as embodied in amethod for computing a target setting value, it is not intended to belimited to the details shown, since various modifications and structuralchanges may be made without departing in any way from the spirit of thepresent invention.

Without further analysis, the foregoing will so fully reveal the gist ofthe present invention that others can, by applying current knowledge,readily adapt it for various applications without omitting featuresthat, from the standpoint of prior art, fairly constitute essentialcharacteristics of the generic or specific aspects of this invention.

1-20. (canceled)
 21. A control unit for controlling a working unit of aharvesting machine, comprising a number of measured-value inputs foracquiring operating-result measured values (MR, MK, MV) of variousoperating-result parameters of the working unit; a curve calculatingunit for computing operating-result curves (KR, KK, KV) for the variousoperating-result parameters, each of which is based on a number of theoperating-result measured values (MR, MK, MV) of a particularoperating-result parameter acquired at various setting values of acertain control parameter (SG, SO, SU) of the working unit; a targetsetting value detection unit for computing a target setting value (ZG,ZO, ZU) adapted to a harvesting process for the control parameter (SG,SO, SU) based on a combination of the operating-result curves (KR, KK,KV) of the various computed-operating result parameters; and a controlparameter output for controlling an operation selected from the groupconsisting of controlling the working unit based on the computed targetsetting value (ZG, ZO, ZU); offering the computed target setting value(ZG, ZO, ZU) to an operator to use in controlling the working unit, andboth.
 22. A control unit as defined in claim 21; and further comprisinga terminal interface for connection to a control terminal for acquiringdefault values (VW) for computing a corresponding one of the targetsetting values (ZG, ZO, ZU).
 23. A harvesting machine, comprising acontrol unit as defined in claim 21;
 24. A combine harvester, comprisinga control unit as defined in claim
 21. 25. (canceled)